Weekend Project: Virtual Startup Team

Notebook: week3_multi_agent/project_startup_simulator.ipynb

The Big Question

Can a team of gpt-4o-mini agents outperform a single gpt-4o genius?

In this project, we put this to the test by building a Virtual Startup Team where specialized agents collaborate to create a business pitch deck.

Project Overview

We'll compare two approaches:

Approach	Description
Solo GPT-4o	One powerful model does everything
Team of Minis	CEO, CTO, CMO (all `gpt-4o-mini`) collaborate

Architecture: Supervisor Pattern

The CEO acts as a Supervisor who coordinates the team:

The Challenge

Startup Idea: "Uber for Dog Walking in rural areas"

Requirements:

Core Value Proposition
Technology Stack
Go-to-Market Strategy

Implementation

Step 1: Define Your Agents

AGENTS = {
    "CEO": {
        "model": "gpt-4o-mini",
        "role": "You are the CEO. You coordinate the team and consolidate outputs."
    },
    "CTO": {
        "model": "gpt-4o-mini",
        "role": "You are the CTO. Focus on technology stack, scalability, and engineering feasibility."
    },
    "CMO": {
        "model": "gpt-4o-mini",
        "role": "You are the CMO. Focus on target audience, marketing channels, and virality."
    }
}

Step 2: Solo Approach

def solo_run(idea):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a startup expert."},
            {"role": "user", "content": f"Idea: {idea}\n{REQUIREMENTS}"}
        ]
    )
    return response.choices[0].message.content

Step 3: Team Approach

def ask_agent(role_name, task):
    agent_cfg = AGENTS[role_name]
    response = client.chat.completions.create(
        model=agent_cfg["model"],
        messages=[
            {"role": "system", "content": agent_cfg["role"]},
            {"role": "user", "content": task}
        ]
    )
    return response.choices[0].message.content
 
# 1. CTO analyzes tech
cto_out = ask_agent("CTO", f"Plan the tech stack for: {STARTUP_IDEA}")
 
# 2. CMO plans marketing
cmo_out = ask_agent("CMO", f"Plan marketing for: {STARTUP_IDEA}")
 
# 3. CEO synthesizes everything
final_prompt = f"""
Synthesize these reports into a 1-page pitch.
 
[Tech Report]
{cto_out}
 
[Marketing Report]
{cmo_out}
"""
team_result = ask_agent("CEO", final_prompt)

Why Multi-Agent Works

Factor	Solo Model	Team of Agents
Perspective	Single viewpoint	Multiple specialized viewpoints
Context	One huge context window	Focused, smaller contexts
Cost	1× GPT-4o call	3× GPT-4o-mini (still cheaper!)
Quality	Good, but generic	Specialized insights per domain

Key Insight: The CTO agent naturally thought about "Rural Connectivity" (offline mode) because of its persona, whereas a generic single prompt might miss this domain-specific consideration.

Results Comparison

Solo GPT-4o Output

# RoverRural - Uber for Dog Walking in Rural Areas

## 1. Core Value Proposition
RoverRural revolutionizes pet care in rural communities...
[Generic, comprehensive response]

Team of Minis Output

# Rural Rover - One-Page Pitch

## Overview
Combining technology with community engagement...

## Tech Stack (from CTO)
- Offline-first architecture for poor connectivity
- SMS fallback for notifications
- Low-bandwidth optimized app

## Marketing (from CMO)
- Partner with local vets and pet stores
- Community ambassador program
- Word-of-mouth in tight-knit communities

Extend the Project

Try these challenges:

Add a CFO Agent

Create a CFO agent to calculate burn rate and funding needs

Implement Debate Mode

Let agents critique each other's proposals before final synthesis

Add Tool Access

Give agents access to web search for market research

Use CrewAI

Refactor the project using CrewAI for cleaner orchestration

Key Takeaways

Specialization beats generalization - Focused agents produce domain-specific insights
Context isolation - Each agent works in a clean, focused context
Cost efficiency - Multiple small models can be cheaper than one large model
Emergent collaboration - The synthesized output often exceeds what either approach could achieve alone

References & Further Reading

Multi-Agent Debate Paper Society of Mind MetaGPT

What's Next?

Congratulations on completing Week 3! You've learned:

Router agents for task delegation
Collaboration patterns between agents
Supervisor hierarchies
MCP for standardized tool integration
CrewAI for rapid multi-agent development

In Week 4, we'll focus on Production Engineering—taking your agents from prototype to production with evaluation, guardrails, and deployment.

Run the Notebook

jupyter notebook week3_multi_agent/project_startup_simulator.ipynb

06. Advanced (Supervisor)Overview